Wireless communication systems transmit and receive data signals within some spectrum of electromagnetic frequencies. As the demand for wireless communication continues to grow, there are a number of challenges inherent in improving the efficiency of the usage of a spectrum, as the capacity of that spectrum is limited. To this end, a number of wireless communication techniques have been proposed. One such system is Multiple-Input Multiple-Output (MIMO), which refers to a class of wireless communication systems that use techniques for sending and receiving more than one data signal simultaneously over the same radio channel. In some embodiments, a MIMO system may include multiple antennas at the transmitting device, receiving device, or both. MIMO techniques improve communications performance by either combating or exploiting multipath scattering in the communications channel between a transmitting device and receiving device. A MIMO system may combat multipath fading by creating what is called spatial diversity, or it may exploit multipath propagation by performing spatial multiplexing. MIMO systems are included in most wireless communications commercial standards including IEEE 802.11n (WiFi), IEEE 802.16e (WiMAX), LTE (3.9G), LTE-Advanced (4G), 802.11 ac (Enhanced WiFi).
Typically, signals transmitted from a transmitter device to a receiver device may be distorted by noise. In order to account for this noise, in conventional MIMO systems, extracting the information at the receiving device side typically requires knowledge of, or at least an estimate of, the channel information between all antennas at both the transmitting device and the receiving device. This channel information is typically represented by a matrix H. In most existing systems, the information detection process in MIMO communications systems is performed by estimating the channel matrix Ĥ, and using it to equalize received data symbols. A maximum likelihood detection (MLD) is then applied in order to extract the information symbols from the received signals. The matrix H is inverted and multiplied by the received signal. And finally, the product outcome is fed to a detector to recreate the information symbols. However, the processes performed by these conventional systems suffer from a number of problems. For example: accurate estimation of the channel matrix Ĥ at the receiving device is a complex process, and it may affect the spectral efficiency of the overall system; the inversion of the estimated channel matrix Ĥ^ is computationally costly, particularly for large number of transmitting antennas; multiplying the inverse of Ĥ, denoted as [Ĥ]−1, by the received signal (equalization), and then using maximum likelihood detectors to extract the information symbols is also computationally costly; and imperfect estimation of H may cause severe performance degradation.
Embodiments of the invention address these and other problems, individually and collectively.